Techniques are disclosed to leverage the use of neural networks or similar machine learning algorithms to de-noise highly accelerated Wave-CAIPIRINHA scans. The described techniques facilitate the generation of 3D sequences using a greatly reduced scan time, with the resulting images having a high spatial resolution and an improved SNR compared to conventional approaches.
Legal claims defining the scope of protection, as filed with the USPTO.
1. A method, comprising: accessing, via one or more processors, an under-sampled dataset included in a Wave dataset, the under-sampled dataset being associated with a magnetic resonance imaging (MRI) scan, the Wave dataset corresponding to two added oscillating wave gradients thereby yielding a Wave dataset having a corkscrew trajectory in k-space; generating, via the one or more processors, a reconstructed dataset from the under-sampled dataset; training, via the one or more processors, a convolutional neural network by calculating filter weights that result in the convolutional neural network providing an output image that substantially matches a difference image; propagating, via the one or more processors, the reconstructed dataset through the trained convolutional neural network to generate one or more de-noised output images; and displaying, via the one or more processors, the one or more de-noised output images.
2. The method of claim 1 , wherein the under-sampled dataset includes a Wave-controlled aliasing in volumetric parallel imaging (CAIPIRINHA) dataset.
3. The method of claim 2 , wherein the Wave dataset further includes at least one fully-sampled dataset that is equivalent to a ground truth.
4. The method of claim 3 , wherein the difference image is a result of a subtraction of the ground truth and an accelerated MRI scan that produces the under-sampled dataset.
5. The method of claim 1 , wherein the act of generating the reconstructed under-sampled dataset includes the use of a Wave-controlled aliasing in parallel imaging (CAIPI) reconstruction.
6. The method of claim 5 , wherein the act of generating the reconstructed under-sampled dataset includes the use of a gradient calibration.
7. The method of claim 1 , further comprising: simulating, via the one or more processors, at least a portion of the Wave dataset by convolving a fully-sampled No-Wave scan with a Wave Point-Spread-Function (PSF) and applying retrospective under-sampling.
8. The method of claim 4 , wherein the difference image resulting from the subtraction of the ground truth and the accelerated MRI scan yields only white noise.
9. The method of claim 4 , wherein the accelerated scans include the use of retrospectively under-sampling the at least one fully-sampled scan without the use of image registration.
10. The method of claim 5 , wherein the act of propagating the reconstructed dataset through the trained convolutional neural network to generate one or more de-noised output images is part of a residual learning procedure.
11. A convolutional neural network, comprising: a storage medium configured to store an under-sampled dataset included in a Wave dataset, the under-sampled dataset being associated with a magnetic resonance imaging (MRI) scan, the Wave dataset corresponding to two added oscillating wave gradients thereby yielding a Wave dataset having a corkscrew trajectory in k-space; and one or more processors configured to: generate a reconstructed dataset from the under-sampled dataset; train a convolutional neural network by calculating filter weights that result in the convolutional neural network providing an output image that substantially matches a difference image; propagate the reconstructed dataset through the trained convolutional neural network to generate one or more de-noised output images; and display the one or more de-noised output images.
12. The convolutional neural network of claim 11 , wherein the under-sampled dataset includes a Wave-controlled aliasing in volumetric parallel imaging (CAIPIRINHA) dataset.
13. The convolutional neural network of claim 12 , wherein the Wave dataset further includes at least one fully-sampled dataset that is equivalent to a ground truth.
14. The convolutional neural network of 10 , wherein the difference image is a result of a subtraction of the ground truth and an accelerated MRI scan that produces the under-sampled dataset.
15. The convolutional neural network of claim 11 , wherein the one or more processors are further configured to generate the reconstructed under-sampled dataset using a Wave-controlled aliasing in parallel imaging (CAIPI) reconstruction.
16. The convolutional neural network of claim 15 , wherein the one or more processors are further configured to generate the reconstructed under-sampled dataset using a gradient calibration.
17. The convolutional neural network of claim 11 , wherein the one or more processors are further configured to simulate at least a portion of the Wave dataset by convolving a fully-sampled No-Wave scan with a Wave Point-Spread-Function (PSF) and applying retrospective under-sampling.
18. The convolutional neural network of claim 11 , wherein the convolutional neural network is associated with a U-NET architecture.
19. A non-transitory computer-readable medium associated with a convolutional neural network, the non-transitory medium having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to: access an under-sampled dataset included in a Wave dataset, the under-sampled dataset being associated with a magnetic resonance imaging (MRI) scan, the Wave dataset corresponding to two added oscillating wave gradients thereby yielding a Wave dataset having a corkscrew trajectory in k-space; generate a reconstructed dataset from the under-sampled dataset; train the convolutional neural network by calculating filter weights that result in the convolutional neural network providing an output image that substantially matches a difference image; propagate the reconstructed dataset through the trained convolutional neural network to generate one or more de-noised output images; and display the one or more de-noised output images.
20. The non-transitory computer-readable medium of claim 19 , wherein: the under-sampled dataset includes a Wave-controlled aliasing in volumetric parallel imaging (CAIPIRINHA) dataset, the Wave dataset further includes at least one fully-sampled dataset that is equivalent to a ground truth, and the difference image is a result of a subtraction of the ground truth and an accelerated MRI scan that produces the under-sampled dataset.
21. The non-transitory computer-readable medium of claim 19 , further storing instructions that, when executed by one or more processors, cause the one or more processors to generate the reconstructed under-sampled dataset using a Wave-controlled aliasing in parallel imaging (CAIPI) reconstruction.
22. The non-transitory computer-readable medium of claim 21 , further storing instructions that, when executed by one or more processors, cause the one or more processors to generate the reconstructed under-sampled dataset using a gradient calibration.
23. The non-transitory computer-readable medium of claim 19 , further storing instructions that, when executed by one or more processors, cause the one or more processors to simulate at least a portion of the Wave dataset by convolving a fully-sampled No-Wave scan with a Wave Point-Spread-Function (PSF) and applying retrospective under-sampling.
Cooperative Patent Classification codes for this invention. Click any code to explore related patents in that topic.
March 1, 2019
January 19, 2021
Browse 5M+ US patents with plain-English claim translations and AI-generated analysis.